Intelligent Farming: Crop Recommendation System Powered by Decision Tree Classification

Farm technologies such as crop guidance tools remains very useful in enhancing and improving the yields in farming. The objective of this research is therefore to design a decision tree crop advice mechanism to aid farmers identify high return crops that will thrive in the current land and climate of a given region. This method employs active use of new data analysis to increase farm yields, and reduce on the impact farms have on the environment. It is originate from a website, nitrogen, phosphorus potassium temperature and humidity, ph and rainfall etc are shown there. Decision tree is constructed for analysing the data and to identify patterns, relations and clusters. The suggested method was compared against the existing algorithms like K-Means, K-Medoid; Support Vector Machine (SVM). Decision Tree algorithm had the highest accuracy, at 99%.